Strengthening Indonesia's Cryptocurrency Regulation to Combat Money Laundering: A Comparative Analysis of Canada and South Korea's Approaches
Bibliographic record
Abstract
This paper explores the challenges posed by cryptocurrency-based money laundering in Indonesia and the need for enhanced legislation to address this growing threat. It highlights the gaps in the current regulatory framework, which lacks specific provisions targeting the unique risks of digital currencies. By comparing the regulatory approaches of Canada and South Korea, the study identifies best practices that Indonesia could adopt to combat cryptocurrency-related crimes. The research emphasizes the importance of implementing targeted legislation for cryptocurrency exchanges, requiring compliance with Anti-Money Laundering (AML) and Counter-Terrorism Financing (CTF) regulations, and introducing Know-Your-Customer (KYC) procedures and real-name account policies to address the anonymity of digital assets. Furthermore, the paper advocates for strengthening international cooperation, utilizing advanced technologies like blockchain analytics, and increasing public awareness and institutional capacity to effectively tackle cryptocurrency-based money laundering. The findings underscore the need for Indonesia to adopt a more comprehensive and technologically forward-thinking legal framework that aligns with global standards to ensure a safer and more transparent digital financial ecosystem. This research contributes to the ongoing discourse on cryptocurrency regulation and offers recommendations for strengthening Indonesia's efforts in combating illicit financial activities within the digital asset space.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".